Enhancing the Engagement of Immigrant and Ethnocultural Minority Clients in Canadian Early Intervention Services for Psychosis
Bibliographic record
Abstract
"The past century has seen significant diversification of the Canadian population.1 Over 20% of the Canadian population is foreign born, and 20% belong to a racial minority group.2 These minority populations add to the fabric of Canadian society and contribute to its economic and social growth. While they often demonstrate significant strengths, as evidenced by the well-documented healthy migrant effect (i.e., immigrants are in better health than native-born populations, at least when they arrive),2 it is also well known that these groups face unique challenges within the mental health care system. The Mental Health Commission of Canada has identified the mental health of immigrants (those who were born outside of Canada), refugees (those who were persecuted in their home country), ethnocultural groups (groups that share common ancestry and cultural characteristics), and racialized groups (a term more commonly used instead of visible minority, stemming from the recognition that race is a social construct3) as a priority.2 Broadly speaking, the Canadian mental health care system and service providers have faced challenges in fully engaging immigrant and ethnocultural minority populations,4–7 who are likelier to seek mental health services after long delays8 and to drop out prematurely.4 This subpar service engagement can have far-reaching consequences for individuals, families, and communities.5 It can contribute to inequalities in mental health treatment and outcome between immigrant and ethnocultural minority clients and the general population. [...]"@eng
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.024 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".